Tropospheric ducting phenomena over the Hellenic region

Isaakidis, Stergios A.; Xenos, Thomas D.; Dris, Nicholas A.

doi:10.1002/dac.646

Cited by 3 publications

(1 citation statement)

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“…The loss of water potential of almonds induced by water stress entails the reduction of tree growth, high leaf shedding, lower kernel weight, and the change in pericarp color. In addition, stomatal conductance and carbon metabolism are reduced (Isaakidis et al, 2004). In a study, rootstocks were exposed to daily irrigation treatments up to field capacity and drought stress for five days in July and August.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Some Selected Cultivars of Almond on GF677 Rootstock in Drought Stress Conditions

Piri¹,

Sedaghathoor

2022

Yüzüncü Yıl Üniversitesi Tarım Bilimleri Dergisi

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Drought stress is the most important factor limiting the successful production of agricultural crops worldwide. The response of some almond cultivars grafted on GF677 rootstock was studied to drought stress at a research greenhouse in the Horticultural Science Research Institute of Karaj, Iran, in the 2016-2017 growing season. The plant materials included 12 almond (Prunus dulcis Mill.) genotypes/cultivars, including ‘Sh10’, ‘Saba’, ‘A1-16’, ‘Shokofeh’, ‘Kh1’, ‘A230’, ‘Mamaie’, ‘A13-40’, ‘A9-7’, ‘A8-24’, ‘Fragiolu’, and ‘Sh17’, which were grafted onto Gf677 rootstock. The results showed that the treated almonds differed significantly. When all studied traits are considered, it can be concluded that genotypes ‘Kh1’ and ‘A13-40’ outperformed all other genotypes and cultivars in terms of the studied morphological and physiological traits and exhibited far more tolerance to drought stress. In normal conditions, cultivars Kh1 and Mamaei had maximum proline (2.35 µmole/g), but A8-24 still showed the lowest content of proline (1.20 µmole/g). The highest K content under drought stress was obtained in A1-16 (2.8 %) and Mamaei (2.71 %) cultivars.

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Section: Introductionmentioning

confidence: 99%

Assessment of Some Selected Cultivars of Almond on GF677 Rootstock in Drought Stress Conditions

Piri¹,

Sedaghathoor

2022

Yüzüncü Yıl Üniversitesi Tarım Bilimleri Dergisi

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Parabolic Wave Equation Method Applied to the Tropospheric Ducting Propagation Problem: A Survey

Sirkova

Mikhalev

2006

Electromagnetics

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An artificial neural network predictor for tropospheric surface duct phenomena

Isaakidis

Dimou

Xenos

et al. 2007

Nonlin. Processes Geophys.

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Abstract. In this work, an artificial neural network (ANN) model is developed and used to predict the presence of ducting phenomena for a specific time, taking into account ground values of atmospheric pressure, relative humidity and temperature. A feed forward backpropagation ANN is implemented, which is trained, validated and tested using atmospheric radiosonde data from the Helliniko airport, for the period from 1991 to 2004. The network's quality and generality is assessed using the Area Under the Receiver Operating Characteristics (ROC) Curves (AUC), which resulted to a mean value of about 0.86 to 0.90, depending on the observation time. In order to validate the ANN results and to evaluate any further improvement options of the proposed method, the problem was additionally treated using Least Squares Support Vector Machine (LS-SVM) classifiers, trained and tested with identical data sets for direct performance comparison with the ANN. Furthermore, time series prediction and the effect of surface wind to the presence of tropospheric ducts appearance are discussed. The results show that the ANN model presented here performs efficiently and gives successful tropospheric ducts predictions.

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Tropospheric ducting phenomena over the Hellenic region

Cited by 3 publications

References 1 publication

Assessment of Some Selected Cultivars of Almond on GF677 Rootstock in Drought Stress Conditions

Assessment of Some Selected Cultivars of Almond on GF677 Rootstock in Drought Stress Conditions

Parabolic Wave Equation Method Applied to the Tropospheric Ducting Propagation Problem: A Survey

An artificial neural network predictor for tropospheric surface duct phenomena

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